PhD Thesis Presentation

Characterization of Intertidal Vegetation on European Coasts Using MultiScale Remote Sensing in Response to Natural and Anthropogenic Pressures

The 15th of May 2025

Thesis supervisor:

Laurent Barillé, Professor

Co-supervisor:

Pierre Gernez, Lecturer

Jury members:

Antoine Collin

Rodney Forster

Evangelos Spyrakos

Bárbara Ondiviela

Federica Braga

Laurent Barillé

Pierre Gernez

Lecturer

Professor

Professor

Senior scientist

Senior Researcher

Professor

Lecturer

École Pratique des Hautes Études (EPHE), Dinard, France

University of Hull, United Kingdom

University of Stirling, United Kingdom

Universidad de Cantabria, Spain

CNR-ISMAR, Venice, Italy

Nantes Université, France

Nantes Université, France

Simon Oiry

Preface

Remote Sensing, Benthic Ecology and Ecotoxicology

  • Benthic ecology and shellfish aquaculture
  • Biodiversity of benthic primary producers
  • Hyperspectral, multispectral and drone remote sensing
  • Ecotoxicology and emerging pollutants
  • Marine optics and ocean colour remote sensing

PhD related projects

BiCOME Project

2021-2024

  • Develop Observation tools
  • Assess impacts of land/sea use, pollution and climate change
  • identify regions of resilience or sensitivity

Project funded by:

REWRITE Project

2024-Actual

Aims to revitalize Europe’s intertidal areas through rewilding, promoting climate resilience, biodiversity, and societal benefits along the European shoreline.

Project funded by:

InvaSea Project

2024-Actual

  • Assessing the Capacity of Remote Sensing to Map Invasive Red Algae
  • Mapping G. vermiculophylla along the French coastline

Project funded by:

Table of Contents

Introduction

Coastal Environments

Areas where the land masses meet the seas

  • Directly in contact with the sea
  • 25km away from the sea
  • 50km away from the sea
  • French Coast are densely populated:
  • 4% of the French territory
  • 10% of the French population
  • Globaly:

1 billion people (15%) within 10km (4%)

~3 billion by 2100

Human activities

Hotspots of Economic Growth

Seaport

Dredging

Aquaculture

Energy Production

Artificialization

  • Fishing activities
  • Tourism
  • Industries

Environmental Impacts

The mark of human activity on nature

Oil spills

Erosion

Alien Species Introduction

Energy introduction

Habitat destruction

  • Fishing activities
  • Pollutions

Intertidal habitats

Living on the edge of land and sea

Areas between high and low tide

Saltmarshes

Mangroves

Polychaete reefs

Rocky reefs

Tidal flats

Oyster reefs

A rich variety of intertidal habitats

Soft-bottom substrats

Guadalquivir River, Spain

  • A - Magnoliopsida
  • B - Bacillariophyceae
  • C - Phaeophyceae
  • D - Florideophyceae
  • E - Chlorophyceae

Five Taxonomic Classes

of Vegetation

Hard-bottom substrats

Vigo, Spain

Saja estuary, Spain

Ecosystem Services

  • Protection against Erosion
  • Carbon fixation
  • Nursery & Shelter
  • Nutrient Fixation

~ $30 trillion per year

Protect these ecosystems:

  • Habitat Directive (1992)
  • Water framework Directive (2001)
  • Marine Strategy Framework Directive (2008)
  • Birds Directive (2009)
  • Nature Restoration Law (2024)

Good knowledge and monitoring to inform policies

Remote Sensing

A tool to map them all !

Traditional sampling methods:

  • Expensive
  • Time consuming
  • Low extent and temporal resolution
  • Hard to access

Remote Sensing:

  • Cost effective
  • Good coverage/Time ratio
  • Synchronous broad-scale view
  • Simplifies the field work

From the sky to the sea

The science of obtaining information about objects or areas from a distance

Applyed to Earth Observation:

Remote Sensing

From the sky to the sea

Resolution Trade-offs

Sentinel-2

Drone

10–60 m spatial resolution

100 000 km²/image

5-day revisit

cm resolution

Adapted to small-scale studies

Flight planning flexibility

Fieldwork remains essential to make sense of what satellites see

Radiometric calibration

Aven, France

Ground truthing

Noirmoutier, France

Features georeferencing

Tainaron, Greece

Sampling

Cadiz, Spain

Monitoring coastal change from space

Coastal Remote Sensing: A trendy topic !

  • Standardized measurements or indicators to monitor biodiversity
  • Adapted for remote sensing applications

Objectives of this work

Show how remote sensing can effectively map intertidal habitats and assess environmental pressures

Analyzing the potential of multispectral sensors to distinguish macrophytes in soft-bottom intertidal zones at low tide

Building an algorithm that discriminates the most common taxonomic classes of vegetation found on soft bottom intertidal sediment

Investigate the capacity of remote sensing to monitor intertidal vegetation under abiotic and biotic pressures

Developing Advanced Methodologies for Intertidal Vegetation Monitoring

Challenges to map intertidal vegetation

Introduction to Spectroradiometry

\[R(\lambda) = \frac{L_{\text{up}}(\lambda)}{L_{\text{down}}(\lambda)}\]

  • \(\lambda\) is the Wavelenght
  • \(L_{\text{up}}\) is the upwelling radiance
  • \(L_{\text{down}}\) is the downwelling radiance

\[R_i^*(\lambda) = \frac{R_i(\lambda) - \min(R_i)}{\max(R_i) - \min(R_i)}\]

  • \(R_i(\lambda)\) is the reflectance the the wavelength \(\lambda\) of the spectrum \(R_i\)
  • \(min(R_i)\) and \(max(R_i)\) are the minimum and maxium reflectance of the spectrum \(R_i\)
  • Each spectrum is between 0 and 1

ASD FieldSpec Handheld 2

Hyperspectral Sensor

A lot of Narrow Spectral Bands

  • Is it possible to discriminate green macrophytes using remote sensing techniques ?
  • What is the impact of the spectral resolution on the discrimination accuracy ?

Material & Methods

Building a Spectral library of intertidal vegetation

Total of 332 Spectra of 5 taxonomic classes

2 instruments

  • GER 3700 and ASD Fieldspec handheld 2

Calibration

  • Optimisation of the integration time
  • Dark noise calibration
  • Measurement of Radiance with a 99% Spectralon white reference

Sampling method

  • Operator angled at 90° to the sun
  • At least 10 replicate for each sample
  • 30 to 50cm from the ground
  • Field of view of the instrument set to ~ 3.5°

\(R(\lambda)_{\rm sample} = \frac{1}{n}\sum_{i=1}^{n}R_i(\lambda), \quad\text{with }n \ge 10\)

Spectral degradation

ASD

PRISMA

Drone

S2 - 20m

Pléiades

S2 - 10m

500 bands

50 Bands

10 Bands

8 Bands

4 Bands

4 Bands

Spectral comparisons

Compare the Spectra:

  • nMDS + ANOSIM for each spectral resolution

Compare the Sensors:

  • Supervised Machine Learning Classifiers
  • Random Forest
  • XGBoost
  • SVM

  • Random Forest

Spliting of the dataset:

  • 75 % for training
  • 25 % for testing

Tuning of hyperparameters:

  • Maximisation of the AUC-ROC

Validation:

  • Accuracy metrics
  • Variable Importance

Putting theory into practice

DJI Matrice 200

Micasense RedEdge-MX Dual

Sentinel-2: 100 pixels/hectar

Drone 120 m: ~1 500 000 pixels/hectar

Drone 12 m: ~15 000 000 pixels/hectar

50% Spectralon

Downwelling Light Sensor

Results

Hyperspectral library

Hyperspectral library - nMDS

Hyperspectral library - Random Forest Classifier

  • Global accuracy: 0.95
  • Cohen’s kappa: 0.93
  • Sensitivity: 0.93
  • Specificity: 0.98

  • Global accuracy: 0.94
  • Cohen’s kappa: 0.93
  • Sensitivity: 0.94
  • Specificity: 0.98

  • Global accuracy: 0.83
  • Cohen’s kappa: 0.79
  • Sensitivity: 0.84
  • Specificity: 0.96

Drone imagery - Example of classification

Chlorophyceae

Bacillariophyceae

Magnoliopsida

Florideophyceae

Chlorophyceae

Bacillariophyceae

Magnoliopsida

Florideophyceae

Drone imagery - Validation

Drone imagery - Variable importance

Discussion

Pigment Composition, Spectral Signature and Variable Importance

Similar pigment composition,…

Distinction between green macrophyte possible, …

  • … and ~530 nm & ~650 nm are key wavelengths

Green macrophytes often co-occurs in intertidal areas…

  • Ultra high spatial resolution (from 80 to 8mm per pixel)

Green macrophytes often co-occurs in intertidal areas…

  • Ultra high spatial resolution (from 80 to 8mm per pixel)
  • Easy Photo-interpretation of pixels
  • More then 1 000 000 training pixels. Over 11 sites of 3 country
  • Diverse training dataset

Drone: 0.26 ha ~ 2.5 millions pixels

S2: 25 000 hectares ~ 2.5x Paris

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Bourgneuf Bay, July 2024

Case Study 1 – Facing Biological Invasions

Ecological Context & Significance

History of the aquaculture of the oyster in Europe

Flat Oyster

Portuguese Oyster

Pacific Oyster

A Hidden Passenger

  • Originated from Japan
  • 10 000 T spat were imported between 1971 and 1973
  • Originated from Japan
  • Transport of fragment of Gracilaria vermiculophylla

Resilient to:

  • Salinity changes
  • Dessication
  • Eutrophic conditions
  • Can attach to shells, rocks or colonize soft bottom areas

Well adapted to European estuaries

Gracilaria vermiculophylla

The transport of acquaculture structures between cultivation sites favorised its spread across the World

The transport of acquaculture structures between cultivation sites favorised its spread across Europe

First observation in Europe in the Belon, Brittany, in 1996

Belon Estuary, France, 2024

Aveiro, Portugal, 2021

Etel, France, 2024

Auray, France, 2024

Scorff, France, 2024

Saja estuary, Spain, 2024

Ecological Impacts of the invasion

Negatives:

  • Can affect native Fucoids and Seagrasses
  • Alter the sediment composition and structure
  • Modify or disrupt trophic interactions

Positives:

  • Create new habitats
  • Stabilize the Sediment

Monitoring and Managing

Remote Sensing as a tool to follow the invasion

Satellite:

  • Follow the invasion over time
  • Go back in time

Drone:

  • Flexibility to monitor the early stages of the invasion
  • Offer an ultra high resolution

Objectives of the work

First description of G. vermiculophylla using remote sensing techniques

Using RS archives to assess historical invasion in the Belon Estuary

Use DISCOV to map G. vermiculophylla and link its spatial distribution to the mudflat topography.

Material & Methods

Historical analysis

Sciences et Techniques, Nantes, 1962

Maps and Aerial photographs archives

  • 8 images between 1952 and 2012
  • 1 Drone flight in 2024

Photo interpretation of images to retrieve the area covered by G. vermiculophylla

Hyperspectral measurments

Same methodology as the spectral library

Second derivative:

\[ f''(\lambda_i) \approx \frac{f(\lambda_{i+1}) - 2f(\lambda_i) + f(\lambda_{i-1})}{(\Delta \lambda)^2} \]

DJI Matrice 300

4 Drone flight over G. vermiculophylla

Micasense RedEdge-MX Dual

DJI Zenmuse L1

2 Instruments:

  • Multispectral camera
  • LiDAR

10 Spectral bands between 444 and 840 nm

  • NIR LiDAR
  • 240 000 points/s
  • ~ 3cm accuracy
  • High resolution RGB camera

ShinyValidate

Presence and absence of red macroalgae for each drone flight

Country

Site

Absent

Present

Total

France

Aven Estuary

1,073

463

1,536

France

Belon Estuary

1,389

443

1,832

Spain

Marisma de Cortiguera

1,531

483

2,014

Spain

Marisma de Cudón

1,237

136

1,373

Total

5,230

1,525

6,755

Digital Surface Model:

  • Map of the Slope of the Mudflat

Generalized Linear Mixed Model

\[ \begin{align*} \mathrm{Cover}_{ij} &\sim \mathrm{Beta}\bigl(\mu_{ij}\,\phi,\,(1-\mu_{ij})\,\phi\bigr),\\[1em] \mu_{ij} &= \mathrm{logit}(\eta_{ij}), \\[1em] \eta_{ij} &= \underbrace{\alpha_j}_{\substack{\text{intercept for}\\\text{site }j}} + \underbrace{\beta_1\,\mathrm{Bathymetry}_{ij}}_{\text{effect of elevation}} + \underbrace{\beta_2\,\mathrm{Slope}_{ij}}_{\text{effect of slope}}. \end{align*} \]

Results

Spectral signature of G. vermiculophylla

Average of ~ 100 spectra

Historical records in the Belon estuary

Drone flights

Chlorophyceae

Florideophyceae

Saltmarshes

Presence/Absence of Red Algae: 91.1%

Chlorophyceae

Florideophyceae

Saltmarshes

Topography of the mudflat

RGB Composition

DSM Color Composition

Slope Categorized

Elevation vs Presence of Algae

  • Higher Cover on the Upper Intertidal

  • The steeper the lower the cover

Discussion

First map of the spatial distribution of G. vermiculophylla:

  • It can create large monospecific meadows…
  • … or be mixed with others intertidal vegetations

First map of the spatial distribution of G. vermiculophylla

It has a unique composition of phycobilin pigments

Drone mapping G. vermiculophylla with machine learning

Saja estuary, Spain

Belon estuary, France

It has a unique composition of phycobilin pigments, resulting in:

  • A unique spectral signature for the class
  • Identification at the Class level by DISCOV.
  • A potential need for hyperspectral resolution to increase taxonomic resolution.

G. gracilis

G. vermiculophylla

C. crispus

Distribution linked with the topography

  • Inhabit the upper intertidal
  • Resistant to dessication, light and salinity variations

Distribution linked with the topography

  • Inhabit the upper intertidal
  • Resistant to dessication, light and salinity variations
  • Inhabit flat areas…
  • …experiencing lower current velocity during tidal exchanges

Invasion phases

Lag Phase

  • Very low abundance
  • Need for genetic or mutualistic adjustment
  • Eradication is feasible and cheapest

Expansion Phase

  • Near‑exponential increase in cover
  • Control effort and cost rise sharply
  • Priority shifts to containment and protection of high‑value sites

Saturation Phase

  • Percent cover reaches a plateau, growth limited by space/resources
  • Ecosystem impacts stabilise but persist
  • Focus turns to long‑term suppression, impact mitigation, and restoration rather than eradication

Short Lag phase

  • Large number of fragments/individuals has been introduced repeatedly in the environment
  • G. vermiculophylla is well adapted to European estuaries and already suited to local climate
  • Ignored by local species, no grazing

Remote Sensing can monitor early stages of the invasion…

  • Making it a powerfull tool for early decision making
  • Facilitates timely interventions

Case Study 2 – Mapping the impact of Heatwaves on intertidal seagrasses

Introduction

Browning of seagrasses across Europe

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Aveiro Lagoon, Portugal, June 2022

Quiberon, France, September 2021

What’s in the litterature ?

On subtidal Zostera marina and Cymodocea nodosa:

  • Highly vulnerable to elevated sea temperatures in winter and spring, leading to early flowering, high mortality, and reduced biomass.
  • Highly vulnerable to elevated sea temperatures in winter and spring, leading to early flowering, high mortality, and reduced biomass.
  • Photosynthetic activity rises during HWs but diminishes during recovery, impairing performance and reducing leaf biomass.
  • Highly vulnerable to elevated sea temperatures in winter and spring, leading to early flowering, high mortality, and reduced biomass.
  • Photosynthetic activity rises during HWs but diminishes during recovery, impairing performance and reducing leaf biomass.
  • Responses vary greatly between species…
  • Highly vulnerable to elevated sea temperatures in winter and spring, leading to early flowering, high mortality, and reduced biomass.
  • Photosynthetic activity rises during HWs but diminishes during recovery, impairing performance and reducing leaf biomass.
  • Responses vary greatly between species…
  • …and within a single species across latitudes.

What about Zostera noltei ?

Impact on the reflectance ?

Impact of Extreme Atmospheric temperature ?

Extreme Temperature Events = Heatwaves

Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years

Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years

Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years

Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years

Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years

Hypothesis & Objectives

Heatwaves alter the spectral reflectance of Zostera noltei seagrass. This change can be detected using remote sensing.

  • Evaluate the direct impact of heatwave-induced thermal stress on the reflectance of Zostera noltei through controlled experiments.
  • Develop a spectral index for detecting stress-induced changes in seagrass coloration.
  • To apply findings from experimental reflectance changes to satellite-based remote sensing, assessing the spatial extent and temporal dynamics of an heatwave event that occurs in September 2021, in Quiberon, on seagrass meadows.

Material & Methods

Experiment in the Lab

Intertidal chambers from ElectricBlue

Experiment Tank

Storage Tank

  • Air Temperature : from 18 to 60°C
  • Water Temperature : from 8°C to 55°C
  • Programmable tides
  • Programmable lights

Measure variation of seagrass leaves reflectance over time.

Seagrasses inside of a chamber

Hyperspectral measurment every minute in each tank

Control

Treatment

Satellite Mapping of the impact of Heatwaves on seagrasses

Atmospheric heatwave between the 4th of September 2021 and the 7th of September 2021 in Quiberon

3 Sentinel-2 images, level L2A, Low Tide:

  • Before: 1st of September 2021

3 Sentinel-2 images, level L2A, Low Tide:

  • Before: 1st of September 2021

  • During: 6th of September 2021

3 Sentinel-2 images, level L2A, Low Tide:

  • Before: 1st of September 2021

  • During: 6th of September 2021

  • After: 8th of October 2021

  • Litto3D product

detailed 3D coastal and nearshore mapping

Both Experimental and Satellite Mapping

  • Well established radiometric Indices:

\[ f''(\lambda_i) \approx \frac{f(\lambda_{i+1}) - 2f(\lambda_i) + f(\lambda_{i-1})}{(\Delta \lambda)^2} \]

\[NDVI = \frac{R(NIR)-R(Red)}{R(NIR)+R(Red)}\]

\[GLI = \frac{[R(Green)-R(Red)]+[R(Green)-R(Blue)]}{(2 \times R(Green) )+ R(Red) + R(Blue) }\]

  • Introduction of the Seagrass Heat Shock Index (SHSI):

\[ \text{SHSI} = I_{SHSI} - R(740) \]

\[ I_{SHSI} = R(560) + \tau [R(842) - R(560)] \]

\[ \tau = \frac{740 - 560}{842 - 560} \]

Results

Spectral signatures:

SHSI design

Heatwave experiment

Spectral metrics:

\(R''_{665 \, \text{nm}}\) drops by 68 %

\(NDVI\) drops by 31 %

\(GLI\) drops by 54 %

\(SHSI\) increases by 420 %

in situ Satellite Mapping

Before

During

Before

During

After

General conclusions and future perspectives

  • Duality Drone and Satellite (Strength, weaknesses, complementary of methods)
  • Remote sensing for coastal ecosystem Management
  • Future direction (RS for aquaculture, penology, restoration…)
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